Search NASA⌕ Search

SEARCH · Search NASA

Results for “biomedical data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

F-15B QuietSpike(TradeMark) Aeroservoelastic Flight Test Data Analysis

System identification or mathematical modelling is utilised in the aerospace community for the development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. The reason for utilising a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance for the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data for several flight conditions (Mach number) that (i) linear models are inefficient for modelling aeroservoelastic data, (ii) nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data and (iii) the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗
NASA NTRS1977

Instrumentation for space flight experiments

The selection of measurement systems for experiments conducted in the context of a space flight must be guided by the criteria applicable to any scientific study requiring objective measurements of physiological variables. Steps fundamental to the process of choosing the best instrumentation system are identified and the key factors in matching the operational characteristics of the instrumentation to its intended use are discussed. Special problems in obtaining data from nonhuman primates, whether restrained or unrestrained, are explored. Choices for data processing are evaluated as well as the use of prototype flight tests and simulations to assess future life science experiments for spacelab or payloads for the space shuttle biomedical scientific satellite.

Mccutcheon, E. P.↗
NASA NTRS1973

Extravehicular activities guidelines and design criteria

A listing of astronaut EVA support systems and equipment, and the physical, operational, and performance characteristics of each major system are presented. An overview of the major ground based support operations necessary in the development and verification of orbital EVA systems is included. The performance and biomedical characteristics of man in the orbital EV environment are discussed. Major factors affecting astronaut EV work performance are identified and delineated as they relate to EV support systems design. Data concerning the medical and physiological aspects of spaceflight on man are included. The document concludes with an extensive bibliography, and a series of appendices which expand on some of the information presented in the main body.

Brown, N. E.↗
NASA NTRS2002

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

NASA Discipline Cardiopulmonary↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRS2013

Low Profile Tunable Dipole Antennas Using BST Varactors for Biomedical Applications

In this presentation a 2.4 GHz low profile (lambda45) tunable dipole antenna is evaluated in the presence of a human core model (HCM) body phantom. The antenna uses a frequency selective surface (FSS) with interdigital barium strontium titanate (BST) varactor-tuned unit cells and its performance is compared to a similar low profile antenna that uses an FSS with semiconductor varactor diodes. The measured data of the antenna demonstrate tunability from 2.2 GHz to 2.55 GHz in free space and impedance match improvement in the presence of a HCM at different distances. This antenna has smaller size, lower cost and less weight compared to the semiconductor varactor diode counterpart.

Tunable Dipole Antennas↗
NASA NTRSDate not supplied

Capturing, Analyzing, Maintaining, and Disseminating Shape Memory Material Data Between Information Management Systems

With an increased demand on reducing the time, cost, and effort to develop new materials, Integrated Computational Materials Engineering (ICME) has received widespread attention in various engineering disciplines as a catalyst for significantly reducing experimental testing during the material design process. An ICME approach to design can enable ‘fit-for-purpose’ materials to be realized in engineering applications by incorporating well-understood process-property-performance relationships between the various length and time scales in a material’s structure, enabling material optimization. However, such an approach requires validated multiscale models at the various length scales for a material, which in turn requires a large amount of data, a robust means of storing the data, and the ability to link data to developed material models. The NASA Vision 2040 [1] has identified nine key elements to enabling ICME approaches in system level design, with one being “Data, Information, and Visualization”, thus outlining the importance of a robust information management system for ICME. As the relationship between microstructure, properties, and material performance become better understood and incorporated into multiscale models that can be leveraged in application design, the emergence of new materials with application-driven properties can be realized. One such new material class that has seen growing attention are shape memory materials (SMM), in which a material can transition between a deformed and undeformed state via a reversible phase transformation when subject to a thermal, mechanical, or magnetic load [2]. SMMs have been used widely in aerospace and biomedical industries, including applications such as actuators, low-shock mechanisms, medical staples, braces, and stents [3, 4]. These materials exhibit unique behavior due to their ability to transition between phases, and thus the mechanisms that enable this transition must be captured in a data information management system and incorporated into SMM material models. At NASA Glenn Research Center, the Shape Memory Materials Database (SMMD) Tool has been developed to capture the necessary information that governs SMM material behavior and provide users the ability to select and visualize various SMMs for a specific application [5]. The database contains point-wise data for published SMM materials, along with the pedigree metadata for traceability necessary for a robust information management system. The database is also capable of storing in-house test data performed at NASA GRC by interacting with the developed Shape Memory Alloy (SMA) Analytics tool to extract the necessary point-wise values and populate the database. Although the SMMD Tool offers its users a single, authoritative source for SMM material data that is critical for model development and material design, the full material pedigree of the in-house test data for SMMs is not currently captured and is out of the scope for the SMMD tool. In this work, the schema for capturing SMM test data within the larger NASA GRC ICME Schema [6, 7, 8, 9] will be developed and implemented for thermomechanical tests conducted at NASA GRC. The developed schema will not only store the relevant data needed for the SMMD tool, but also the material pedigree (i.e., production of the bulk material, bulk material analysis, sample cut-out diagrams, sample fabrication procedure, etc.), test pedigree (i.e., test equipment used, measurement systems used, raw test data), and analysis pedigree (i.e., how the data in the SMMD tool is calculated). Furthermore, a Python-based framework will be developed to seamlessly interact between the SMA Analytics and SMMD tools, which will write the full dataset and associated metadata to the GRC Information Management System before passing the required point-wise data to the SMMD tool. Data informatics is a key element of the NASA Vision 2040, which requires not only that data is stored and maintained throughout the material lifecycle, but that the data is also accessible and reusable such that material development efforts can be minimized. Therefore, for an ICME design approach to be realized, a centralized information management system that drives the ICME process must be able to communicate with other databases. The work that will be presented in this presentation will therefore not only demonstrate the ability of NASA GRC’s information management system to capture SMM data, but also its ability to interact with pre-existing tools specialized for such materials.

Data management↗
NASA NTRS1971

Apollo 15 Mission Report

A detailed discussion is presented of the Apollo 15 mission, which conducted exploration of the moon over longer periods, greater ranges, and with more instruments of scientific data acquisition than previous missions. The topics include trajectory, lunar surface science, inflight science and photography, command and service module performance, lunar module performance, lunar surface operational equipment, pilot's report, biomedical evaluation, mission support performance, assessment of mission objectives, launch phase summary, anomaly summary, and vehicle and equipment descriptions. The capability of transporting larger payloads and extending time on the moon were demonstrated. The ground-controlled TV camera allowed greater real-time participation by earth-bound personnel. The crew operated more as scientists and relied more on ground support team for systems monitoring. The modified pressure garment and portable life support system provided better mobility and extended EVA time. The lunar roving vehicle and the lunar communications relay unit were also demonstrated.

Source record↗
NASA NTRS1979

Biomedical systems analysis program

Biomedical monitoring programs which were developed to provide a system analysis context for a unified hypothesis for adaptation to space flight are presented and discussed. A real-time system of data analysis and decision making to assure the greatest possible crew safety and mission success is described. Information about man's abilities, limitations, and characteristic reactions to weightless space flight was analyzed and simulation models were developed. The predictive capabilities of simulation models for fluid-electrolyte regulation, erythropoiesis regulation, and calcium regulation are discussed.

Source record↗
NASA NTRS1974

Biomedical programs operations plans

Operational guidelines for the space shuttle life sciences payloads are presented. An operational assessment of the medical experimental altitude test for Skylab, and Skylab life sciences documentation are discussed along with the operations posture and collection of space shuttle operational planning data.

Walbrecher, H. F.↗
NASA NTRS1999

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

NASA Discipline Neuroscience↗
NASA NTRS1998

Evaluation of dual-tip micromanometers during 21-day implantation in goats

Investigative research efforts using a cardiovascular model required the determination of central circulatory haemodynamic and arterial system parameters for the evaluation of cardiovascular performance. These calculations required continuous beat-to-beat measurement of pressure within the four chambers of the heart and great vessels. Sensitivity and offset drift, longevity, and sources of error for eight 3F dual-tipped micromanometers were determined during 21 days of implantation in goats. Subjects were instrumented with pairs of chronically implanted fluid-filled access catheters in the left and right ventricles, through which dual-tipped (test) micromanometers were chronically inserted and single-tip (standard) micromanometers were acutely inserted. Acutely inserted sensors were calibrated daily and measured pressures were compared in vivo to the chronically inserted sensors. Comparison of the pre- and post-gain calibration of the chronically inserted sensors showed a mean sensitivity drift of 1.0 +/- 0.4% (99% confidence, n = 9 sensors) and mean offset drift of 5.0 +/- 1.5 mmHg (99% confidence, n = 9 sensors). Potential sources of error for these drifts were identified, and included measurement system inaccuracies, temperature drift, hydrostatic column gradients, and dynamic pressure changes. Based upon these findings, we determined that these micromanometers may be chronically inserted in high-pressure chambers for up to 17 days with an acceptable error, but should be limited to acute (hours) insertions in low-pressure applications.

Non-NASA Center↗
NASA NTRS1996

Facilities for Biological Research Aboard the International Space Station

A centrifuge designed as part of an integrated biological facility for installation onboard the International Space Station is presented. The requirements for the 2.5 m diameter centrifuge, which is designed for the support of biological experiments are discussed. The scientific objectives of the facility are to: provide a means of conducting fundamental studies in which gravitational acceleration is a controllable variable; provide a 1g control; determine the threshold acceleration for physiological response, and determine the value of centrifugation as a potential countermeasure for the biomedical problems associated with space flight. The implementation of the facility is reported on, and the following aspects of the facility are described: the host resources systems supply requirements such as power and data control; the habitat holding rack; the life sciences glove box; the centrifuge; the different habitats for cell culture, aquatic studies, plant research and insect research; the egg incubator, and the laboratory support equipment.

Souza, Kenneth A.↗
NASA NTRS2014

Pilot Sensorimotor and Cardiovascular Results from the Joint Russian/U.S. Field Test

The primary goal of this research is to determine functional abilities associated with long-duration space flight crews beginning as soon after landing as possible (< 2 hours) with an additional two follow-up measurements sessions on the day of landing. This goal has both sensorimotor and cardiovascular elements, including evaluations of NASA's new anti-orthostatic compression garment and the Russian Kentavr garment. Functional sensorimotor measurements will include, but are not limited to, assessing hand/eye coordination, standing from a seated position (sit-to-stand), walking normally without falling, measurement of dynamic visual acuity, discriminating different forces generated with both the hands and legs, recovering from a fall (standing from a prone position), coordinated walking involving tandem heel-to-toe placement, and determining postural ataxia while standing. The cardiovascular portion of the investigation includes measuring blood pressure and heart rate during a timed stand test in conjunction with postural ataxia testing (quiet stance sway) as well as cardiovascular responses during the other functional tasks. In addition to the immediate post-landing collection of data for the full FT, postflight data is being acquired twice more within the 24 hours after landing and will continue over the subsequent weeks until functional sensorimotor and cardiovascular responses have returned to preflight normative values. The PFT represents a initial evaluation of the feasibility of testing in the field, and is comprised of a jointly agreed upon subset of tests from the full FT and relies heavily on Russia's Institute of Biomedical Problems Sensory-Motor and Countermeasures Laboratories for content and implementation. The PFT has been collected on several ISS missions. Testing on the U.S. side has included: (1) a sit-to-stand test, (2) recovery from a fall where the crewmember began in the prone position on the ground and then stood for 3 minutes while cardiovascular stability was determined and postural ataxia data were acquired, and (3) a tandem heel-to-toe walk test to determine changes in the central locomotor program. Video, cardiovascular parameters (heart rate and blood pressure), data from bodyworn inertial sensors, and severity of postflight motion sickness were collected during each test session. Our Russian investigators have added measurements associated with: (a) obstacle avoidance, (b) muscle compliance and (c) postural adjustments to perturbations (push) applied to the subject's chest area. The level of functional deficit observed in the crew tested to date is typically beyond what was expected and is clearly triggered by the acquisition of gravity loads immediately after landing when the demands for crew intervention in response to emergency operations will be greatest. Clearly measureable performance parameters such as ability to perform a seat egress, recover from a fall or the ability to see clearly when walking, and related physiologic data (orthostatic responses) are required to provide an evidence base for characterizing programmatic risks and the degree of variability among crewmembers for exploration missions where the crew will be unassisted after landing. Overall, these early functional and related physiologic measurements will allow the estimation of nonlinear sensorimotor and cardiovascular recovery trends that have not been previously captured

Reschke, M. F.↗
NASA NTRS1975

Automated system for integration and display of physiological response data

The system analysis approach was applied in a study of physiological systems in both 1-g and weightlessness, for short and long term experiments. A whole body, algorithm developed as the first step in the construction of a total body simulation system is described and an advanced biomedical computer system concept including interactive display/command consoles is discussed. The documentation of the design specifications, design and development studies, and user's instructions (which include program listings) for these delivered end-terms; the reports on the results of many research and feasibility studies; and many subcontract reports are cited in the bibliography.

Source record↗
NASA NTRS1978

Statistical considerations in design of spacelab experiments

After making an analysis of experimental error sources, statistical models were developed for the design and analysis of potential Space Shuttle experiments. Guidelines for statistical significance and/or confidence limits of expected results were also included. The models were then tested out on the following proposed Space Shuttle biomedical experiments: (1) bone density by computer tomography; (2) basal metabolism; and (3) total body water. Analysis of those results and therefore of the models proved inconclusive due to the lack of previous research data and statistical values. However, the models were seen as possible guides to making some predictions and decisions.

Robinson, J.↗